Traditional scoring models rely on static rules and historical averages, limiting accuracy in dynamic environments. AI scoring uses machine learning to evaluate behaviour, patterns and real-time signals across multiple variables. This improves decision accuracy in areas such as risk assessment, prioritisation and personalisation, enabling faster responses while continuously learning from outcomes.